A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real-world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real-world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis.

Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service / Mariani, S.; Lahr, M. M. H.; Metting, E.; Vargiu, E.; Zambonelli, F.. - In: INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. - ISSN 0884-8173. - 36:11(2021), pp. 6763-6790. [10.1002/int.22568]

Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service

Mariani S.;Zambonelli F.
2021

Abstract

A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real-world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real-world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis.
2021
36
11
6763
6790
Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service / Mariani, S.; Lahr, M. M. H.; Metting, E.; Vargiu, E.; Zambonelli, F.. - In: INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. - ISSN 0884-8173. - 36:11(2021), pp. 6763-6790. [10.1002/int.22568]
Mariani, S.; Lahr, M. M. H.; Metting, E.; Vargiu, E.; Zambonelli, F.
File in questo prodotto:
File Dimensione Formato  
main.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 2.29 MB
Formato Adobe PDF
2.29 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1251712
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact